All informations about „From order to delivery: the journey of a fashion item“ at Zalando Corporate ✓ Current information about Zalando SE

Zalando: From order to delivery: the journey of a fashion item | Zalando Corporate

boerse.de - Europas erstes Finanzportal bietet Ihnen die aktuellsten Börsen-Nachrichten im Minutentakt, aufgeteilt in die sieben wichtigsten Börsen-Segmente.

BARCLAYS: Zalando "hold" - boerse.de

Part Two TATIANAPW PA - Trousers - midnight sail/dark blue for €149.95 (20/11/2023). Free shipping on most orders*

Part Two TATIANAPW PA - Trousers - midnight sail/dark blue - Zalando.ie

Charli stars in the first Zalando Cover Story, a series chronicling the fashion lives of some of the world’s biggest style icons. As Zalando takes us on a tour of ‘Planet XCX’, from Charli’s grungy beginnings to her current minimalist phase, there are opportunities to shop pieces from the singer’s own moodboard.

Zalando And Charli XCX Want To Tell You A Story

In today’s article, we have used the TipRanks Earnings Calendar to identify two companies, BASF SE (DE:BAS) and Zalando SE (DE:ZAL), from the DAX 40. These companies are scheduled to report their third-quarter earnings for 2023 this week.

DAX 40 Earnings This Week: Zalando and BASF in Focus

As an investor, mistakes are inevitable. But really big losses can really drag down an overall portfolio. So spare

Investors three-year losses continue as Zalando (ETR:ZAL) dips a further 3.0% this week, earnings continue to decline

In the age of exchange-traded funds, or ETFs, many investors think that mutual funds are the kind of old-timey investments that were good for our grandfathers but useless to us in the 21st century. They say mutual funds are overpriced,

9 Best Mutual Funds to Buy Now

The Spanish group will buy a "significant portion" of Ambercycle's recycled polyester textile over the next three years, and get the tech to scale at the company's new factory.

Zara Parent Company Inditex Signs 70 Million Euro Purchase Agreement for Cycora

Germany stocks were mixed after the close on Monday, as gains in the Food & Beverages, Transportation & Logistics and Media sectors led shares higher while losses in the Construction, Pharmaceuticals & Healthcare and

Germany stocks mixed at close of trade; DAX up 0.02%

Brands can store their goods within Zalando’s network of 12 warehouses and 20 return centers and access a single pool of inventory.

Zalando Gets into the B2B Fulfillment Game

Zalando SE

zalando

How Zen Principles Can Revolutionize Your Data Strategy

Rethinking Data Leadership: Beyond Dashboards and Towards True Business Impact

The Data Revolution: AI Takes the Wheel

With the explosive growth of ChatGPT, discussions around how to integrate AI have made their way into corporate boardrooms and product roadmaps at a rapid pace. In fact, AI already plays a vital role in our daily lives. Billions of people spend hours every day glued to apps such as Instagram, TikTok, Facebook, and Twitter, all of which use AI ranking algorithms to personalize your feed. These algorithms analyze thousands of data points per second to predict what a user would like to see most. And the key to getting personalization right is user data. Except, when dealing with user data, it is critical to uphold user privacy laws and protect users from breaches. The mainstream public is still shaken by how used people’s personal data to maneuver politics. Cambridge Analytica According to a report by the Identity Theft Resource Center (ITRC), the US experienced a record number of 1,862 data breaches in 2021. Prominent companies such as Microsoft, LinkedIn, Facebook, Plex, and Cash App experienced significant in just the last two years. data breaches Fortunately, product experts like Sunil Tej Gorantla, who are passionate about building products, are focusing on keeping user data sacrosanct. Having graduated from VIT University, one of the top engineering institutions in India, with a bachelor’s degree in Information Technology, Sunil has a technical background. With more than a decade of experience in product management, Sunil has helped various startups build large-scale web and mobile applications that generate millions in revenue. He is a founder, product expert, growth hacker, and writer who believes in solving what people need, making data-driven decisions, and building products with a strong mission at their core to serve others. During his extensive career, Sunil was fortunate to be mentored by some of the industry's eminent personalities, such as Harishankaran Karunanidhi, Hunter Walk, and Justin Kan. Sunil spent the early years of his career working with leading high-growth early-stage startups. He joined HackerRank as the first Product Evangelist immediately after the company became . To acquire HackerRank's first few thousand users, he spearheaded the campus ambassador program. India’s first YCombinator startup He recruited hundreds of student ambassadors who formed Hacker Clubs at their universities to encourage students to solve programming challenges and participate in weekly programming contests online. He also collaborated with companies such as Airbnb, Adobe, Quora, and NetApp, which hired top programming talent from these contests by being sponsors. However, at the time, retaining users after the contests was a challenge for HackerRank. To improve retention, Sunil implemented gamification and designed HackerRank’s first incentive mechanism to keep users hooked to the product. Working with the team, he launched "Hackos"—a term he coined—which are points users earned by solving programming challenges. Users loved this feature so much that the product's retention rate experienced a significant increase. Sunil's experience scaling HackerRank from scratch to the first 200k users has made his skillset attractive to startups worldwide. His ability to articulate unmet user needs and build product features to drive growth led him to places he least expected. As a Growth Hacker, he helped scale Mobilizr, a consumer tech startup in Nigeria. He also worked as Head of Data for a property tech startup in London. Afterward, he joined Global Savings Group (GSG) in Munich, Germany, as the first Product Owner for CupoNation's mobile web application. In just a few months, he launched CupoNation's first mobile web application in 17+ countries, which tripled the revenue generated from mobile users. Today, GSG is one of the most successful ventures from Rocket Internet, which also spun off Delivery Hero, Zalando, HelloFresh, and Jumia. After leaving GSG, Sunil joined Juniqe, a startup that was ranked as one of the top 3 fastest growing companies in Germany at the time. As an experienced Product Manager, he managed two cross-functional teams to rebuild the entire technical stack, scaling the mobile and web applications to handle millions of users. In addition to leading the Conversion Rate Optimization (CRO) team, he was also the stakeholder manager for Marketing, Search Engine Optimization (SEO), and Customer Relationship Management (CRM). During his time at Juniqe, he played a pivotal role in scaling the product from less than a million to €20 million in annual recurring revenue, turning the startup into one of the largest art e-commerce sites in Europe. While exploring strategies to improve conversion rates at Juniqe, Sunil encountered Dynamic Yield (DY), a personalization solution for e-commerce products. His foray into personalization began as he worked on integrating DY with Juniqe to bring features like “Users who bought this also liked” and “Products you might like.” As one of the first handful of customers to try out DY, he worked closely with DY's team to fix their product issues and share feedback to improve their machine learning (ML) algorithms. This gave Sunil a behind-the-scenes look into how ML was deployed at scale and equipped him to suggest strategies that helped DY become successful. Gartner has named DY a leader in the Magic Quadrant for Personalization Engines for the past five consecutive years. Later, DY was first acquired by McDonald's and then by Mastercard to enhance customer experience using personalization. Armed with the experience transforming user experiences using personalization, Sunil currently leverages his expertise in product development and growth hacking to build Gulpie—a personalized food discovery application. The app helps people discover restaurants based on their unique tastes, while also helping restaurants fight against the manipulation of their reviews and empowering reviewers to monetize their expertise. To create Gulpie, Sunil interviewed hundreds of food lovers, food reviewers, and restaurant owners. Through these interviews, he discovered that a lack of trust in restaurant reviews and user data protection leads to inefficiencies in the current restaurant discovery process, leaving many people perplexed and frustrated. To address inefficiencies in the market, Sunil built a state-of-the-art personalized recommendation engine using an ensemble of cutting-edge machine-learning algorithms. This engine provides users with personalized restaurant recommendations based on their individual tastes, helping them make informed decisions without going through a cumbersome search process. While working on the recommendation engine, Sunil gained key insights into how to deduce unique user tastes. He found that gaining users' trust to share their data requires a promise of user data protection and strict privacy measures. Leaders in the industry like Sriram Krishnan—an investor at a16z who previously built product teams at Twitter, Meta, and Snap—share this sentiment. https://twitter.com/sriramk/status/1631779635376828416?s=46&t=m-tchH2RTFFMPB2qJSBrFQ What Sunil Had to Say Sharing his experience working on personalization at Gulpie, Sunil explains: “Before the invention of blockchains, users were unable to truly own their data as centralized entities held control over it. However, with Gulpie, our aim is to empower users to take control of their data and provide them with better visibility into its usage. We understand that users expect product builders to be cognizant of the skepticism people experience when sharing their data with apps. That’s why we focus on enabling personalization while ensuring that users’ data is absolutely not at risk. We anonymize user data before training our machine-learning models. Additionally, with GDPR and other privacy protection regulations in place, it's important for companies to actively monitor and prevent the misuse of user data to avoid situations like the Cambridge Analytica-Facebook scandal.” In an age where large organizations scavenge user data and abuse privacy without consent, product experts like Sunil lead the way in protecting user privacy. As product managers, builders, and founders strive for personalization without sacrificing privacy, they can rely on experts like Sunil who push the boundaries of what’s possible. With his expertise and vision, we can look forward to a future where user privacy is truly protected, and products can provide tailored experiences that put users first.

Expert Product Manager Sunil Tej Gorantla on Personalization Without User Data at Risk

Today, according to Enhance Ventures, there are along with thousands of accelerators and incubators - all offering unique experiences to entrepreneurs. In this article, we are set to explain how a startup - or a venture - studio can help bring your ideas to life. around 780 startup studios There is a common misbelief that simply having a solid idea is enough to start a business. Available funds on their own do not guarantee success, either. While some entrepreneurs lack experience or knowledge, others may need money or well-trained specialists, and certain people are not good at building internal procedures. To navigate a company to success, one needs a full package. This is when incubators and accelerators come to the rescue. There are in the world today. Among the most famous are Y Combinator, Techstars, and 500 Startups. There are numerous companies that have reached the exit stage in those organizations, including , Codeacademy, Udemy, and . no less than 7,000 accelerators and incubators Reddit Canva However, while accelerators and incubators offer many advantages to entrepreneurs, we are currently witnessing a boom of interest for startup studios. They present a different approach to launching startups and have their own funds and in-house experts. Startup studios go the extra mile from being just a mentor or investors - they become co-founders and partners. What is exactly a startup studio? People who work in startup studios know how to launch and scale a business. The examples of Zalando, Delivery Hero, Hello Fresh, Dots, bitly, Octiv, and others speak better than any words, about what a startup can become when entrepreneurs turn to studios. Startup studios bring together entrepreneurs, investors, and experienced specialists in all fields, and creates an ecosystem, essential to build a business. People, who work in a startup studio, are not just investors, but partners of the future company. They are focused on making the idea work and doing that quickly and with a profit. Facts and myths about startup studios Myth №1: “Startup studios are like mentors” Not everyone will engage with your business if you lack an MVP. Accelerators and incubators typically establish a predetermined timeframe for collaborating with startups, a term defined at the onset of cooperation. On the other hand, a startup studio aligns with the entrepreneur from the outset, shepherding the project through its entirety. The studio functions not only as an investor but also as a deeply involved co-founder, invested in the business's outcomes. Myth №2: “Startup studio will help with every project” Claiming to know everything often reveals a lack of true understanding. Seasoned professionals cultivate expertise and specialization in specific domains, nurturing these skills with pride. For instance, TechStars dedicates itself to tech startups, Buildit Accelerator hones in on IoT and hardware, and Startup Wise Guys embraces diverse niches, including FinTech and Cybersecurity. Certain enterprises also concentrate on specific geographic areas; Venture Catalysts, for instance, nurtures Indian startups, while Chinaccelerator, the largest accelerator in Asia, stands as a prime example. At SKL.vc, our approach involves meticulous evaluation of ideas before integration. We direct our attention towards ventures with global transformative potential, such as consumer apps, generative AI, BCI, and other groundbreaking concepts. Myth №3: “A startup studio does not care about the outcome” A venture studio is a co-founder of the future company, so it owns equity in it. Studying reports and listening to presentations about the work done is not something a studio does. The team is focused on building work quickly and efficiently and maintains a hands-on style so that every stage is optimized. The work at SKL.vc is structured as sprints - we set a target, and when we reach it, we analyze the results. This allows us to adjust the strategy on time. Every project is assigned a customized team of experts. They work until the startup is ready to work without the studio on its own. We select professionals with the most suitable set of skills and qualifications to make sure the project has the best human capital. SKL.vc also has vast experience of working with remote teams, that include top experts from all over the world. Myth №4: “People who work in a startup studio make mistakes, just like I, or other entrepreneurs, do” Entrepreneurs within startup studios avoid the trial-and-error route, foregoing learning solely from mistakes. This is due to the studio's adoption of a sequential launch strategy, leveraging past successful experiences to guide startups. Nevertheless, perfection remains elusive, as errors are inherent to all endeavors. Yet, by tapping into the expertise of top professionals in the industry, entrepreneurs can significantly reduce the probability of failure. At SKL.vc, we have curated an exceptional team of extraordinary individuals. This ensemble comprises not only studio founders and experts adept at shepherding startups but also advisors boasting extensive experience in their specialized domains. So, when can a startup studio help you? A studio does not guarantee success. Neither does an accelerator or an incubator. In the end, which has been in the US, has an exit rate of 33.61%. And Y Combinator, which is often named among the most famous accelerators in the world, has an exit rate of 11.15% (among the companies that reached this stage is, for instance, AirBnB) and ranks 16th in the Beta Boom study. There are many great ideas that will not hit the market, and there are numerous reasons for that. But we will address this next time. Angelpad ranked number one accelerator To understand, if you need a studio, consider the following: You have a clear idea and understanding of what you want A startup studio brings together unique experts. The team that works at SKL.vc has experience in venture capital, and marketing, knows how to scale projects, and has experience in other fields that are indispensable when building a business. We can set up internal procedures. We know what every stage must look like and how it must work. We get down to business very quickly without unnecessary meetings and long calls. We need not more than two meetings to make a decision. Sometimes entrepreneurs get so focused on their ideas, that they forget to take a step aside and look at the project from a fresh perspective. The studio, which is involved with every step, may provide such a perspective. You do not know how to build internal processes A venture studio does not focus on one startup but works on multiple startups in parallel. It means that its processes work in a smooth and flawless way. It can be adjusted to any business that the studio takes on board. When selecting to partner with a studio, an entrepreneur gets a professional with specific knowledge about how to build marketing strategy or financial planning, how to establish relationships with investors and make the product, and how to communicate with the stakeholders and others. You do not have a little black book When startups join us at SKL.vc, we always say that our network is your network now. We have access to human capital that is hard to get. And we also assign a superhero mentor to every project! You do not have funds We use our own capital to invest in the startups. When it comes to finding investment, entrepreneurs may take this task off their to-do list - we will do it ourselves. You do not have a team We can build the team from scratch and will make it tailored for the business. From advisors with specific experience relevant to your needs to key experts from different niches of the market that will help your company become a successful enterprise. A startup studio is one option available to entrepreneurs today, along with accelerators and incubators. However, the unique approach that studios offer has made them an attractive partner for those who want to build a successful business in an already crowded market.

Startup Studios: Why Founders Aspire to Join and How They Foster Company Building

Startup Studios: por qué los fundadores aspiran a unirse y cómo fomentan la creación de empresas

Startup Studios : pourquoi les fondateurs aspirent à rejoindre et comment ils favorisent la création d'entreprise

स्टार्टअप स्टूडियो: क्यों संस्थापक शामिल होने की इच्छा रखते हैं और वे कंपनी निर्माण को कैसे बढ़ावा देते हैं

スタートアップ スタジオ: 創業者が参加を熱望する理由と会社構築をどのように促進するか

Startup Studios: por que os fundadores desejam ingressar e como eles promovem a construção da empresa

Startup Studios: Tại sao các nhà sáng lập mong muốn tham gia và cách họ thúc đẩy việc xây dựng công ty

创业工作室：创始人为何渴望加入以及他们如何促进公司建设

Sharing another insightful article by a seasoned software engineer and tech enthusiast Alex Babin (with his permission). *** Keeping up with the latest trends and technological advancements is a crucial responsibility for every CTO. As someone deeply involved in IT development, I have witnessed firsthand the rapid pace at which the industry evolves. This is why the landscape of complex IT projects demands not only technical expertise but also a mastery of managing innovations. In this article, I will share my insights and experiences on modern approaches to tackling the challenges of change management. Common issues with change management Change is an inevitable part of any organization's growth and evolution. However, resistance to change is a natural and common reaction that often emerges during periods of organizational transformation. It stems from various factors, such as fear of the unknown, concerns about job security, loss of control, or simply the comfort of familiar routines. This is why wise change management is crucial. So, what are the common pain points in this regard? Catching up with innovations As a CTO, it's easy to feel overwhelmed by the constant influx of new technologies and methodologies hitting the market almost daily. Keeping up and making informed decisions about which tools and processes to adopt can be a daunting task, but falling behind means potentially missing out on significant benefits that could give your company a competitive edge. It’s also crucial to gain hands-on experience with the latest technologies and tools. For example, this can involve attending industry conferences, participating in training programs, and working on personal projects to stay up-to-date with the latest trends. Dealing with technical debt Managing technical debt over time. Source: Morphis Technical debt occurs when short-term solutions are implemented, often to meet deadlines or cut costs, at the expense of long-term maintainability and scalability. For example, implementing a quick fix to resolve an urgent issue may result in code that is difficult to maintain and may require a complete overhaul in the future. Over time, technical debt accumulates, making it harder to modify, fix, or update the software. Lack of continuous improvement practices The absence of a culture that stipulates continuous improvement can also present challenges for CTOs. Without a commitment to regularly reviewing and refining processes, it's easy for inefficiencies to creep in and hinder overall productivity. Specifically, obsolete tools, methods and frameworks might become a stumbling block to sustainable growth. For instance, the costs of using outdated IT solutions might eventually as compared to implementing more innovative ones. Establishing a culture of continuous improvement can help ensure that your organization stays agile and responsive to change. get higher by 50% Tips and recommendations that outlines your organization's goals, the tools and processes needed to achieve them, as well as the timeline for implementation. This document can help you prioritize innovations and stay focused on your long-term objectives. For example, you can put together a list of technologies and tools you use now, specify outdated ones and those you’d like to adopt in the future. Another example is a technology map, like this by Zalando. Develop a technology roadmap Tech Radar new technologies, methodologies, and industry trends to get first-hand experience which is essential for further advancement. You can also suggest to your team members that they do the same and share their findings. This will help ensure that you're aware of emerging innovations and can make informed decisions about which to adopt. Set aside regular time to explore . Before fully committing to a new technology or innovation, you can carry out a small-scale pilot project to test its feasibility and potential benefits. With this experimental approach, you can better understand the implications of adopting the new technology.Establish a process for addressing technical debt. This may include regular code reviews, refactoring, and allocating resources to tackle technical debt as part of your ongoing development process. For example, employees may dedicate a certain percentage of their time each sprint to address technical debt, ensuring that the codebase remains maintainable and scalable in the long run. Implement the Proof of Concept approach to track the success of new tools, processes, or technologies. This can help you estimate the impact of your decisions and make any necessary adjustments. For instance, if your team implements a new project management tool, you can track KPIs such as the number of completed tasks, project completion time, and team member satisfaction. Implement metrics and KPIs Wrapping up To recap, today I've touched on the importance of mastering change and innovation management and provided some tips on how to tackle typical issues in this regard. When addressing these challenges, it can be also useful to keep in mind the upper-level factors they stem from, such as organizational structure and communication culture in the company, increasing system complexity and balancing business needs with affordable resources and capabilities. I hope the recommendations I shared will be helpful and actionable.

Modern Approaches to Complex IT Development: Mastering Change and Innovation Management

Data powers machine learning algorithms and scikit-learn or offers high quality datasets that are widely used by researchers, practitioners and enthusiasts. Scikit-learn (sklearn)  is a Python module for machine learning built on top of SciPy. It is unique due to its wide range of algorithms, ease of use and integration with other Python libraries. sklearn What are “Sklearn Datasets”? Sklearn datasets are included as part of the scikit-learn ( ) library, so they come pre-installed with the library. Due to this, you can easily access and load these datasets, without having to download them separately. sklearn To use a specific dataset, you can simply import it from sklearn.datasets module and call the appropriate function to load the data into your program. These datasets are usually pre-processed and ready to use, which saves time and effort for data practitioners who need to experiment with different machine learning models and algorithms. Complete List of Datasets in the Sklearn Library Iris Diabetes Digits Linnerud Wine Breast Cancer Wisconsin Boston Housing Olivetti Faces California Housing MNIST Fashion-MNIST make_classification make_regression make_blobs make_moons and make_circles Make_sparse_coded_signal Pre-Installed(Toy) Sklearn Datasets 1. Iris This dataset includes measurements of the sepal length, sepal width, petal length and petal width of 150 iris flowers, which belong to 3 different species: setosa, versicolor and virginica. The iris dataset has 150 rows and 5 columns, which are stored as a dataframe, including a column for the species of each flower. The variables include: Sepal.Length - The sepal.length represents the length of the sepal in centimetres. Sepal.Width - The sepal.width represents the width of the sepal in centimetres. Petal.Length - The petal.length represents the length of the petal in centimetres. Species - The species variable represents the species of the iris flower, with three possible values: setosa, versicolor and virginica. You can load the iris dataset directly from sklearn using the function from the sklearn.datasets module. load_iris # To install sklearn
pip install scikit-learn

# To import sklearn
from sklearn.datasets import load_iris

# Load the iris dataset
iris = load_iris()

# Print the dataset description
print(iris.describe()) Code for loading the Iris dataset using sklearn. Retrieved from on 27/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html 2. Diabetes This sklearn dataset contains information on 442 patients with diabetes, including demographic and clinical measurements: Age Sex Body mass index (BMI) Average blood pressure Six blood serum measurements (e.g. total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol). A quantitative measure of diabetes disease progression (HbA1c). The Diabetes dataset can be loaded using the function from the sklearn.datasets module. load_diabetes() from sklearn.datasets import load_diabetes

# Load the diabetes dataset
diabetes = load_diabetes()

# Print some information about the dataset
print(diabetes.describe()) Code for loading the Diabetes dataset using sklearn. Retrieved from on 28/3/2023. https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset 3. Digits This sklearn dataset is a collection of hand-written digits from 0 to 9, stored as grayscale images. It contains a total of 1797 samples, with each sample is a 2D array of shape (8,8). There are 64 variables (or features) in the digits sklearn dataset, corresponding to the 64 pixels in each digit image. The Digits dataset can be loaded using the function from the sklearn.datasets module. load_digits() from sklearn.datasets import load_digits

# Load the digits dataset
digits = load_digits()

# Print the features and target data
print(digits.data)
print(digits.target) Code for loading the Digits dataset using sklearn. Retrieved from on 29/3/2023. https://scikit-learn.org/stable/datasets/toy_dataset.html#optical-recognition-of-handwritten-digits-dataset 4. Linnerud The Linnerud dataset contains physical and physiological measurements of 20 professional athletes. The dataset includes the following variables: Three physical exercise variables - chin-ups, sit-ups, and jumping jacks. Three physiological measurement variables - pulse, systolic blood pressure, and diastolic blood pressure. To load the Linnerud dataset in Python using sklearn: from sklearn.datasets import load_linnerud
linnerud = load_linnerud() Code for loading the linnerud dataset using sklearn. Retrieved from on 27/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud 5. Wine This sklearn dataset contains the results of chemical analyses of wines grown in a specific area of Italy, to classify the wines into their correct varieties. Some of the variables in the dataset: Alcohol Malic acid Ash Alkalinity of ash Magnesium Total phenols Flavanoids The Wine dataset can be loaded using the function from the sklearn.datasets module. load_wine() from sklearn.datasets import load_wine

# Load the Wine dataset
wine_data = load_wine()

# Access the features and targets of the dataset
X = wine_data.data  # Features
y = wine_data.target  # Targets

# Access the feature names and target names of the dataset
feature_names = wine_data.feature_names
target_names = wine_data.target_names Code for loading the Wine Quality dataset using sklearn. Retrieved from on 28/3/2023. https://scikit-learn.org/stable/datasets/toy_dataset.html#wine-recognition-dataset 6. Breast Cancer Wisconsin Dataset This sklearn dataset consists of information about breast cancer tumours and was initially created by Dr. William H. Wolberg. The dataset was created to assist researchers and machine learning practitioners in classifying tumours as either malignant(cancerous) or benign (non-cancerous). Some of the variables included in this dataset: ID number Diagnosis (M = malignant, B = benign). Radius (the mean of distances from the centre to points on the perimeter). Texture (the standard deviation of gray-scale values). Perimeter Area Smoothness (the local variation in radius lengths). Compactness (the perimeter^2 / area - 1.0). Concavity (the severity of concave portions of the contour). Concave points (the number of concave portions of the contour). Symmetry Fractal dimension ("coastline approximation" - 1). You can load the Breast Cancer Wisconsin dataset directly from sklearn using the function from the sklearn.datasets module. load_breast_cancer from sklearn.datasets import load_breast_cancer

# Load the Breast Cancer Wisconsin dataset
cancer = load_breast_cancer()

# Print the dataset description
print(cancer.describe()) Code for loading the Breast Cancer Wisconsin dataset using sklearn. Retrieved from on 28/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html Real World Sklearn Datasets Real world sklearn datasets are based on real-world problems, commonly used to practice and experiment with machine learning algorithms and techniques using the sklearn library in Python. 7. Boston Housing The Boston Housing dataset consists of information on housing in the area of Boston, Massachusetts. It has about 506 rows and 14 columns of data. Some of the variables in the dataset include: CRIM - Per capita crime rate by town. ZN - The proportion of residential land zoned for lots over 25,000 sq.ft. INDUS - The proportion of non-retail business acres per town. CHAS - Charles River dummy variable (= 1 if tract bounds river; 0 otherwise). NOX - The nitric oxide concentration (parts per 10 million). RM - The average number of rooms per dwelling. AGE - The proportion of owner-occupied units built prior to 1940. DIS - The weighted distances to five Boston employment centres. RAD - The Index of accessibility to radial highways. TAX - The full-value property-tax rate per $10,000. PTRATIO - The pupil-teacher ratio by town. B - 1000(Bk - 0.63)^2 where -Bk is the proportion of blacks by town. LSTAT - The percentage lower status of the population. MEDV - The median value of owner-occupied homes in $1000's. You can load the Boston Housing dataset directly from scikit-learn using the function from the sklearn.datasets module. load_boston from sklearn.datasets import load_boston

# Load the Boston Housing dataset
boston = load_boston()

# Print the dataset description
print(boston.describe()) Code for loading the Boston Housing dataset using sklearn. Retrieved from on 29/3/2023. https://scikit-learn.org/0.15/modules/generated/sklearn.datasets.load_boston.html 8. Olivetti Faces The Olivetti Faces dataset is a collection of grayscale images of human faces taken between April 1992 and April 1994 at AT&T Laboratories. It contains 400 images of 10 individuals, with each individual having 40 images shot at different angles and different lighting conditions. You can load the Olivetti Faces dataset in sklearn by using the function from the datasets module. fetch_olivetti_faces from sklearn.datasets import fetch_olivetti_faces

# Load the dataset
faces = fetch_olivetti_faces()

# Get the data and target labels
X = faces.data
y = faces.target Code for loading the Olivetti Faces dataset using sklearn. Retrieved from on 29/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_olivetti_faces.html 9. California Housing This sklearn dataset contains information on median house values, as well as attributes for census tracts in California. It also includes 20,640 instances and 8 features. Some of the variables in the dataset: MedInc - The median income in block. HouseAge - The median age of houses in block. AveRooms - The average number of rooms per household. AveBedrms - The average number of bedrooms per household. Population - The block population. AveOccup - The average household occupancy. Latitude - The latitude of the block in decimal degrees. Longitude - The longitude of the block in decimal degrees. You can load the California Housing dataset using the function from sklearn. fetch_california_housing from sklearn.datasets import fetch_california_housing

# Load the dataset
california_housing = fetch_california_housing()

# Get the features and target variable
X = california_housing.data
y = california_housing.target Code for loading the California Housing dataset using sklearn. Retrieved from on 29/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html 10. MNIST The MNIST dataset is popular and widely used in the fields of machine learning and computer vision. It consists of 70,000 grayscale images of handwritten digits 0–9, with 60,000 images for training and 10,000 for testing. Each image is 28x28 pixels in size and has a corresponding label denoting which digits it represents. You can load the MNIST dataset from sklearn using the following code: from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784') Note: The MNIST dataset is a subset of the Digits dataset. Code for loading the MNIST dataset using sklearn. Retrieved from on 30/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_openml.html#sklearn.datasets.fetch_openml 11. Fashion-MNIST The Fashion MNIST dataset was created by Zalando Research as a replacement for the original MNIST dataset. The Fashion MNIST dataset consists of 70,000 grayscale images(training set of 60,000 and a test set of 10,000) of clothing items. The images are 28x28 pixels in size and represent 10 different classes of clothing items, including ­T-shirts/tops, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots. It is similar to the original MNIST dataset, but with more challenging classification tasks due to the greater complexity and variety of the clothing items. You can load this sklearn dataset using the fetch_openml function. from sklearn.datasets import fetch_openml

fmnist = fetch_openml(name='Fashion-MNIST') Code for loading the Fashion MNIST dataset using sklearn. Retrieved from__ __ on 30/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_openml.html#sklearn.datasets.fetch_openml Generated Sklearn Datasets Generated sklearn datasets are synthetic datasets, generated using the sklearn library in Python. They are used for testing, benchmarking and developing machine learning algorithms/models. 12. make_classification This function generates a random n-class classification dataset with a specified number of samples, features, and informative features. Here's an example code to generate this sklearn dataset with 100 samples, 5 features, and 3 classes: from sklearn.datasets import make_classification

X, y = make_classification(n_samples=100, n_features=5, n_informative=3, n_classes=3, random_state=42) This code generates a dataset with 100 samples and 5 features, with 3 classes and 3 informative features. The remaining features will be redundant or noise. Code for loading the make_classification dataset using sklearn. Retrieved from on 30/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification 13. make_regression This function generates a random regression dataset with a specified number of samples, features, and noise. Here's an example code to generate this sklearn dataset with 100 samples, 5 features, and noise level of 0.1: from sklearn.datasets import make_regression

X, y = make_regression(n_samples=100, n_features=5, noise=0.1, random_state=42) This code generates a dataset with 100 samples and 5 features, with a noise level of 0.1. The target variable y will be a continuous variable. Code for loading the make_regression dataset using sklearn. Retrieved from on 30/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression 14. make_blobs This function generates a random dataset with a specified number of samples and clusters. Here's an example code to generate this sklearn dataset with 100 samples and 3 clusters: from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=100, centers=3, random_state=42) This code generates a dataset with 100 samples and 2 features (x and y coordinates), with 3 clusters centred at random locations, and with no noise. Code for loading the make_blobs dataset using sklearn. Retrieved from on 30/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs 15. and make_moons make_circles These functions generate datasets with non-linear boundaries that are useful for testing non-linear classification algorithms. Here's an example code for loading the make_moons dataset: from sklearn.datasets import make_moons X, y = make_moons(n_samples=1000, noise=0.2, random_state=42) This code generates a dataset with 1000 samples and 2 features (x and y coordinates) with a non-linear boundary between the two classes, and with 0.2 standard deviations of Gaussian noise added to the data. Code for loading the make_moons dataset using sklearn. Retrieved from on 30/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html#sklearn.datasets.make_moons Here's an example code to generate and load the make_circles dataset: from sklearn.datasets import make_circles

X, y = make_circles(n_samples=1000, noise=0.05, random_state=42) Code for loading the make_circles dataset using sklearn. Retrieved from on 30/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles 16. make_sparse_coded_signal This function generates a sparse coded signal dataset that is useful for testing compressive sensing algorithms. Here's an example code for loading this sklearn dataset: from sklearn.datasets import make_sparse_coded_signal

X, y, w = make_sparse_coded_signal(n_samples=100, n_components=10, n_features=50, n_nonzero_coefs=3, random_state=42) This code generates a sparse coded signal dataset with 100 samples, 50 features, and 10 atoms. Code for loading the make_sparse_coded_signal dataset using sklearn. Retrieved from on 30/3/2023. https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_coded_signal.html#sklearn-datasets-make-sparse-coded-signal Common Use Cases for Sklearn Datasets Pre-Installed(Toy) Sklearn Datasets - This sklearn dataset is commonly used for classification tasks and is used as a benchmark dataset for testing classification algorithms. Iris - This dataset contains medical information about patients with diabetes and is used for classification and regression tasks in healthcare analytics. Diabetes - This sklearn dataset contains images of handwritten digits and is commonly used for image classification and pattern recognition tasks. Digits - This dataset contains physical fitness and medical data of 20 athletes and is commonly used for multivariate regression analysis. Linnerud - This sklearn dataset contains chemical analysis of wines and is commonly used for classification and clustering tasks. Wine - This dataset contains medical information about breast cancer patients and is commonly used for classification tasks in healthcare analytics. Breast Cancer Wisconsin Real World Sklearn Datasets - This sklearn dataset contains information about housing in Boston and is commonly used for regression tasks. Boston Housing - This dataset contains grayscale images of faces and is commonly used for image classification and facial recognition tasks. Olivetti Faces - This sklearn dataset contains information about housing in California and is commonly used for regression tasks. California Housing - This dataset contains images of handwritten digits and is commonly used for image classification and pattern recognition tasks. MNIST - This sklearn dataset contains images of clothing items and is commonly used for image classification and pattern recognition tasks. Fashion-MNIST Generated Sklearn Datasets - This dataset is a randomly generated dataset for binary and multiclass classification tasks. make_classification - This dataset is a randomly generated dataset for regression tasks. make_regression - This sklearn dataset is a randomly generated dataset for clustering tasks. make_blobs and - These datasets are randomly generated datasets for classification tasks and are commonly used for testing nonlinear classifiers. make_moons make_circles - This dataset is a randomly generated dataset for sparse coding tasks in signal processing. make_sparse_coded_signal Final Thoughts Sklearn datasets provide a convenient way for developers and researchers to test and evaluate machine learning models without having to manually collect and preprocess data. They are also available for anyone to download and use freely. The lead image of this article was generated via model using the prompt 'iris dataset'. HackerNoon's AI Stable Diffusion More Dataset Listicles: Excel Datasets Keras Datasets R Datasets

16 Best Sklearn Datasets for Building Machine Learning Models

বিল্ডিং মেশিন লার্নিং মডেলের জন্য 16 সেরা Sklearn ডেটাসেট

Die 16 besten Sklearn-Datensätze zum Erstellen von Modellen für maschinelles Lernen

Los 16 mejores conjuntos de datos de Sklearn para crear modelos de aprendizaje automático

16 meilleurs ensembles de données Sklearn pour créer des modèles d'apprentissage automatique

मशीन लर्निंग मॉडल बनाने के लिए 16 सर्वश्रेष्ठ स्केलेरन डेटासेट

機械学習モデルの構築に最適な 16 の Sklearn データセット

기계 학습 모델 구축을 위한 16가지 최고의 Sklearn 데이터 세트

16 melhores conjuntos de dados do Sklearn para construir modelos de aprendizado de máquina

Makine Öğrenimi Modelleri Oluşturmak için En İyi 16 Sklearn Veri Kümesi

16 bộ dữ liệu Sklearn tốt nhất để xây dựng mô hình học máy

用于构建机器学习模型的 16 个最佳 Sklearn 数据集

Capturing web data from e-commerce websites is very common. Although each website has its own information displayed in the different parts of the UI (product-list page, product-detail page, cart, etc), it is extremely valuable to . identify a standard structure Advantages of using a standardized approach The advantages of adopting a standard structure are enormous when we need coordination between multiple extractions from different websites or, generally speaking, to create a resilient data extraction timeline that remains unchanged to all possible variations that even a single website can have over time. We want to be able to decouple the extraction of data from the acquisition in a database or a data warehouse. At databoutique.com we have gone through several iterations of the ideal data structure for capturing information from websites, and we came to the following conclusions (feel free to comment or, even better, join our conversation on bout this): Discord Data structures need to be standardized a given industry (let’s say fashion, or pharmaceuticals, groceries, electronics) within Data structures must be specific (or different) across the same industries. There can be different structures depending on what level the information is captured (product-list page PLP, vs. product-detail page PDP, or cart page). This point is related to the costs of accessing the different parts of the website. The additional advantage of organizing data structures this way is to help address the final user’s request by being transparent on where they want the information to be captured, thus aligning their request to the cost of actually acquiring it. Advantages of decoupling PLP and PDP processes (and field list) Accessing PDP requires first accessing the PLP. This is why having a PLP structure different than the PDP structure is important. Let’s imagine we want to crawl daily the products of a fashion website like Zalando. While some information might change daily (like product prices or product availability), others are static, like product description and product image (let’s leave out in this example the availability of product sizes, this will be treated separately). We could scan daily all PLP pages and only once each product page PDP when we need to capture the details on each product. Let’s do some math and see the difference. Assumptions: The cost in terms of bandwidth and proxy are similar for scraping a single PLP or a PDP (the actual cost here is irrelevant, we are interested in the comparison) The number of products that appear on Zalando website during a year are 1 million (event this aspect is irrelevant, but helps framing the case) The average number of items contained on a single page of product-list page PLP is 80 items 80% of the PLP pages are filled (meaning, we have 80 items in almost every page, some pages do not list 80 items because there are no more items to show for this particular category In the case we were to crawl daily the product detail page (PDP), we would have to access daily all pages of the product list PLP (15k pages= 1M items/80 items per page / 80% fill rate), to then pass on to each product detail page PDP (1M), for a total of 370M pages (1M X 365 + 15k > 365) scraped per year. If we were to crawl PLP daily, and PDP only once, we would have to crawl only 6M pages a year (15k X 365 +1M). That’s 55 times (!!) cheaper, or if you prefer if the cost per page was 0.0001 USD/page, it would cost 6.7k USD to scrape Zalando for a year through the PLP page vs. 370k USD/year with the daily PDP approach. And this is very important to transfer it to the final user, so it is transparent that if she or he wants daily refreshes from information that is available only on the PDP page, the cost for data only would be 50 times higher. Product-List Page categories A product list page, in the different versions we encountered, can be categorized in: Categorized lists: where the product is presented within a certain category tree that can either be stable or dynamic (but we’ll get into that later). A nice example is industry specific websites, such as fashion stores, electronic stores and so on, where the website somehow assists the user in finding what she or he is looking for, but doesn’t know yet the exact product. Nike.com is a good example for this: the store guides us into our shopping experience through the categories, to display products that are in our area of interest. We can off-course use the search bar, but this is secondary in the UX, as we can see from the positioning of the search bar itself. Uncategorized lists: where products are not displayed to be searched by category but by in-site search. Amazon is a good example of this. Although there is a category tree, the main UX happens via the search bar. We will consider the Categorized lists for now and keep in mind we want a structure to crawl the PLP, not entering the Product Detail Page (PDP) for the moment. This is particularly helpful in large websites, with near or above a million products, where the number of pages crawled varies by several orders of magnitude between PLP and PDP. PLP standardized field list Now on the data structure. There is no golden standard, our decisions came from benchmarking many e-commerce websites and extracting the best structure in common to all of them. we have different groups of information we need: technical info the field structure version the website we are capturing the timestamp or date the info was captured context info what country/ geography does the acquisition refer to what currency are the prices expressed in content category level 1 category level 2 category level 3 brand product code product title full price discounted price reference fields imageurl PDP URL Note: not all websites will have three levels of category, or in some cases, not all branches of the category tree will have a complete length. In both cases, we accept “n.a.” as unavailable in the fields. For websites with branches with more depth than three levels (but to a maximum of 4 or 5) we accept concatenation of the last levels in the category level 3. In the Data Boutique version, we use additional fields related to our business model. Check the for future updates. complete version of this field list Also published . here

Why You Should Identify a Standard Structure When Capturing Web Data from E-Commerce Webs

Computer vision is a field experiencing significant growth and has numerous practical applications, ranging from self-driving cars to facial recognition systems. However, one of the major challenges in this field is obtaining high-quality datasets to train machine learning models. To address this challenge, torchvision provides access to pre-built datasets, models and transforms specifically designed for computer vision tasks. Torchvision also supports both CPU and GPU acceleration, making it a flexible and powerful tool for developing computer vision applications. What are “Torchvision Datasets”? Torchvision datasets are collections of popular datasets commonly used in computer vision for developing and testing machine learning models. With torchvision datasets, developers can train and test their machine learning models on a range of tasks, such as image classification, object detection, and segmentation. The datasets are also preprocessed, labeled and organized into formats that can be easily loaded and used. List of the Torchvision Datasets MNIST CIFAR-10 CIFAR-100 ImageNet COCO Fashion-MNIST SVHN STL-10 CelebA Pascal VOC Places365 1. MNIST This torchvision dataset is popular and widely used in the fields of machine learning and computer vision. It consists of 70,000 grayscale images of handwritten digits 0–9, with 60,000 images for training and 10,000 for testing. Each image is 28x28 pixels in size and has a corresponding label denoting which digits it represents. To access this dataset, you can download it directly from or load the dataset using torchvision: Kaggle import torchvision.datasets as datasets
# Load the training dataset
train_dataset = datasets.MNIST(root='data/', train=True, transform=None, download=True)
# Load the testing dataset
test_dataset = datasets.MNIST(root='data/', train=False, transform=None, download=True) Code for loading MNIST dataset using PyTorch torchvision package. Retrieved from . on 20/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST 2. CIFAR-10 The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. It has a total of 50,000 training images and 10,000 test images which is further divided into five training batches and one test batch, each with 10,000 images. This dataset can be downloaded from , or loaded here using torchvision: Kaggle import torch
import torchvision
import torchvision.transforms as transforms
 
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
 
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                 	                   download=True, transform=transform)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
 
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2) Note that you can adjust the batch size and number of worker processes for the data loaders as needed. Code for loading CIFAR-10 dataset using PyTorch torchvision package. Retrieved from . on 20/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.CIFAR10.html#torchvision.datasets.CIFAR10 3. CIFAR-100 The CIFAR-100 dataset has 60,000(50,000 training images and 10,000 test images) 32x32 colour images in 100 classes, with 600 images per class. The 100 classes are grouped into 20 super-classes, with a fine label to denote its class and a coarse label to represent the super-class that it belongs to. To download the torchvision dataset from Kaggle, please visit the Kaggle and follow the instructions provided there. Alternatively, if you prefer to load the dataset using the torchvision library, you can do this: website import torchvision.datasets as datasets
import torchvision.transforms as transforms
# Define transform to normalize data
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
 
# Load CIFAR-100 train and test datasets
trainset = datasets.CIFAR100(root='./data', train=True, download=True, transform=transform)
testset = datasets.CIFAR100(root='./data', train=False, download=True, transform=transform)
 
# Create data loaders for train and test datasets
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False) Code for loading CIFAR-100 dataset using PyTorch torchvision package. Retrieved from . on 20/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.CIFAR100.html#torchvision.datasets.CIFAR100 4. ImageNet The ImageNet dataset in torchvision contains approximately 1.2 million training images, 50,000 validation images and 100,000 test images. Each image in the dataset is labeled with one of the 1,000 categories such as "cat," "dog," "car,", "airplane" etc. To download this torchvision dataset, you have to visit the or load in torchvision: website import torchvision.datasets as datasets
import torchvision.transforms as transforms
 
# Set the path to the ImageNet dataset on your machine
data_path = "/path/to/imagenet"
 
# Create the ImageNet dataset object with custom options
imagenet_train = datasets.ImageNet(
	root=data_path,
	split='train',
    transform=transforms.Compose([
        transforms.Resize(256),
        transforms.RandomCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.229, 0.224, 0.225])
	]),
	download=False
)
 
imagenet_val = datasets.ImageNet(
	root=data_path,
	split='val',
    transform=transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.229, 0.224, 0.225])
	]),
	download=False
)
 
# Print the number of images in the training and validation sets
print("Number of images in the training set:", len(imagenet_train))
print("Number of images in the validation set:", len(imagenet_val)) Code for loading ImageNet dataset using PyTorch torchvision package. Retrieved from . on 21/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.ImageNet.html#torchvision.datasets.ImageNet 5. MS Coco The Microsoft Common Objects in Context(MS Coco) dataset contains 328,000 high-quality visual images of everyday objects and humans, often used as a standard to compare the performance of algorithms in real-time object detection. To download this torchvision dataset, please visit the or load in torchvision: website import torch
from torchvision import datasets, transforms
# Define transformation
transform = transforms.Compose([
   transforms.Resize(256),
   transforms.CenterCrop(224),
   transforms.ToTensor(),
   transforms.Normalize(mean=[0.485, 0.456, 0.406],
                        std=[0.229, 0.224, 0.225])
])
 
# Load training dataset
train_dataset = datasets.CocoDetection(root='/path/to/dataset/train2017',
                                       annFile='/path/to/dataset/annotations/instances_train2017.json',
                                       transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
 
# Load validation dataset
val_dataset = datasets.CocoDetection(root='/path/to/dataset/val2017',
                                     annFile='/path/to/dataset/annotations/instances_val2017.json',
                                     transform=transform)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False) Make sure to replace the /path/to/dataset placeholders with the actual path to your dataset directory. Also, adjust the batch_size parameter to fit your needs. Code for loading MS Coco dataset using PyTorch torchvision package. Retrieved from . https://pytorch.org/vision/stable/generated/torchvision.datasets.CocoDetection.html#torchvision.datasets.CocoDetection on 21/3/2023 6. Fashion-MNIST The Fashion MNIST dataset was created by Zalando Research as a replacement for the original MNIST dataset. The Fashion MNIST dataset consists of 70,000 grayscale images (training set of 60,000 and a test set of 10,000) of clothing items. The images are 28x28 pixels in size and represent 10 different classes of clothing items, including ­T-shirts/tops, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots. It is similar to the original MNIST dataset, but with more challenging classification tasks due to the greater complexity and variety of the clothing items. This torchvision dataset can be downloaded from , or loaded using this code: Kaggle import torch
import torchvision
import torchvision.transforms as transforms

# Define transformations
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))])
# Load the dataset
trainset = torchvision.datasets.FashionMNIST(root='./data', train=True,
                                        download=True, transform=transform)

testset = torchvision.datasets.FashionMNIST(root='./data', train=False,
                                       download=True, transform=transform)

# Create data loaders
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2) Code for loading Fashion-MNIST dataset using PyTorch torchvision package. Retrieved from . on 21/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.FashionMNIST.html#torchvision.datasets.FashionMNIST 7. SVHN The SVHN (Street View House Numbers) dataset is an image dataset derived from Google’s Street View imagery, which consists of cropped images of house numbers taken from street-level images. It is available in a full format with all the house numbers and their bounding boxes and a cropped format with just the house numbers alone. The full format is often used for object detection tasks, while the cropped format is commonly used for classification tasks. The SVHN dataset is also included in the torchvision package and it contains 73,257 images for training, 26,032 images for testing and 531,131 additional images for extra training data. To download this torchvision dataset, you can go to or you can load it here: Kaggle import torchvision
import torch

# Load the train and test sets
train_set = torchvision.datasets.SVHN(root='./data', split='train', download=True, transform=torchvision.transforms.ToTensor())
test_set = torchvision.datasets.SVHN(root='./data', split='test', download=True, transform=torchvision.transforms.ToTensor())

# Create data loaders
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=False) Code for loading SVHN dataset using PyTorch torchvision package. Retrieved from . on 22/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.SVHN.html#torchvision.datasets.SVHN 8. STL-10 The STL-10 dataset is an image recognition dataset that consists of 10 classes, with a total of about 6,000+ images. The STL-10 stands for “Standard Training and Test Set for Image Recognition-10 classes” and the 10 classes in the dataset are: Airplane Bird Car Cat Deer Dog Horse Monkey Ship Truck To access this dataset, you can download it directly from or load the dataset using torchvision: Kaggle import torchvision.datasets as datasets
import torchvision.transforms as transforms

# Define the transformation to apply to the data
transform = transforms.Compose([
    transforms.ToTensor(),   

# Convert PIL image to PyTorch tensor
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))   # Normalize the data
])

# Load the STL-10 dataset
train_dataset = datasets.STL10(root='./data', split='train', download=True, transform=transform)
test_dataset = datasets.STL10(root='./data', split='test', download=True, transform=transform) Code for loading STL-10 dataset using PyTorch torchvision package. Retrieved from . on 22/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.STL10.html#torchvision.datasets.STL10 9. CelebA This torchvision dataset is a popular large-scale face attributes dataset which comprises over 200,000 celebrity images. It was first released by researchers at the Chinese University of Hong Kong in 2015. An image in the CelebA consists of 40 facial attributes such as age, hair colour, facial expression and gender. Also, these images were retrieved from the internet and cover a wide range of facial appearances, including different races, ages and genders. Bounding box annotations for the location of the face in each image, as well as 5 landmark points for the eyes, nose and mouth. You can download this dataset on or load it using this code: Kaggle import torchvision.datasets as datasets
import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.CenterCrop(178),
    transforms.Resize(128),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
celeba_dataset = datasets.CelebA(root='./data', split='train', transform=transform, download=True) Code for loading CelebA dataset using PyTorch torchvision package. Retrieved from . on 22/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.CelebA.html#torchvision.datasets.CelebA 10. PASCAL VOC The VOC dataset (Visual Object Classes) was first introduced in 2005 as part of the PASCAL VOC Challenge, which aimed at advancing the state of the art in visual recognition. It consists of images of 20 different object categories, including animals, vehicles, and common household objects. Each of these images is annotated with the locations and classifications of objects within the image. The annotations include both bounding boxes and pixel-level segmentation masks. The dataset is split into two main sets: the training and validation sets. The training set contains approximately 5,000 images with annotations, while the validation set contains around 5,000 images without annotations. In addition, the dataset also includes a test set with approximately 10,000 images, but the annotations for this set are not publicly available. To access the recent dataset, you can download from the , or load it in torchvision: website Kaggle import torch
import torchvision
from torchvision import transforms

# Define transformations to apply to the images
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Load the train and validation datasets
train_dataset = torchvision.datasets.VOCDetection(root='./data', year='2007', image_set='train', transform=transform)
val_dataset = torchvision.datasets.VOCDetection(root='./data', year='2007', image_set='val', transform=transform)
# Create data loaders
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False) Code for loading PASCAL VOC dataset using PyTorch torchvision package. Retrieved from . on 22/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.VOCDetection.html#torchvision.datasets.VOCDetection 11. Places365 The Places365 dataset is a large-scale scene recognition dataset with more than 1.8 million images covering 365 scene categories. The Places365 Standard dataset consists of around 1.8 million images, while the Places365-Challenge dataset contains 50,000 additional validation images that are more challenging for recognition models. To access this dataset, you can use or load in torchvision here: Kaggle import torch
import torchvision
from torchvision import transforms

# Define transformations to apply to the images
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Load the train and validation datasets
train_dataset = torchvision.datasets.Places365(root='./data', split='train-standard', transform=transform)
val_dataset = torchvision.datasets.Places365(root='./data', split='val', transform=transform)
# Create data loaders
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False) Code for loading Places365 dataset using PyTorch torchvision package. Retrieved from . on 22/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.Places365.html#torchvision.datasets.Places365 Common Use Cases for Torchvision Datasets - This dataset is commonly used for image classification tasks, particularly for handwritten digit recognition. MNIST - These datasets are commonly used for image classification tasks, particularly for object recognition. and CIFAR-10 CIFAR-100 - This torchvision dataset contains millions of images of various objects and animals and is commonly used for object recognition tasks. ImageNet - This dataset is commonly used for object detection, segmentation, and captioning tasks. MS COCO - Similar to MNIST, this dataset is commonly used for image classification tasks, particularly for fashion item recognition. Fashion-MNIST - This dataset is commonly used for digit recognition tasks. It is particularly useful for developing and testing algorithms for recognizing numbers in real-world settings. SVHN - This torchvision dataset is commonly used for image classification tasks, particularly for object recognition. STL-10 - This dataset is commonly used for facial recognition and attribute classification tasks. It consists of images of various facial expressions, poses and backgrounds. CelebA - This dataset is commonly used for object detection and segmentation tasks. Pascal VOC - This dataset is commonly used for scene recognition tasks. It consists of images of various indoor and outdoor scenes such as bedrooms, beaches, and offices. Places365 Final Thoughts Torchvision datasets are often used for training and evaluating machine learning models such as convolutional neural networks (CNNs), which are commonly used in computer vision applications. They are also available for anyone to download and use freely. The lead image of this article was generated via model using the prompt 'thousands of images organized together in small frames'. HackerNoon's AI Stable Diffusion More Dataset Listicles: Excel Datasets Keras Datasets R Datasets PyTorch Datasets Hugging Datasets

11 Torchvision Datasets for Computer Vision You Need to Know

কম্পিউটার ভিশনের জন্য 11 টর্চভিশন ডেটাসেট আপনার জানা দরকার

11 Torchvision-Datensätze für Computer Vision, die Sie kennen müssen

11 conjuntos de datos de Torchvision para visión por computadora que necesita saber

11 ensembles de données Torchvision pour la vision par ordinateur que vous devez connaître

कंप्यूटर विजन के लिए 11 टॉर्चविजन डेटासेट आपको जानना जरूरी है

知っておくべきコンピューター ビジョン用の 11 の Torchvision データセット

당신이 알아야 할 11가지 컴퓨터 비전용 Torchvision 데이터 세트

11 conjuntos de dados Torchvision para visão computacional que você precisa conhecer

Bilgisayarlı Görme için Bilmeniz Gereken 11 Torchvision Veri Kümesi

11 bộ dữ liệu Torchvision cho thị giác máy tính bạn cần biết

你需要知道的 11 个用于计算机视觉的 Torchvision 数据集

Keras provides a high-level API that simplifies the process of building and training complex neural network models. With a wide range of pre-built layers and functions, developers can easily build and train deep learning models on large datasets using optimization algorithms. Keras also supports GPU acceleration for training and inference, making it a popular choice for both research and industry applications. What are “Keras Datasets”? Keras datasets are preprocessed datasets that come pre-installed with the Keras library. These datasets are commonly used in the deep learning community for benchmarking models on various tasks such as image classification, text classification, and regression. By leveraging these datasets, developers can experiment with different deep learning models and easily compare their performance. This article looks at the , accessible to developers and researchers worldwide. Best Keras Datasets for Building and Training Deep Learning Models List of Keras Datasets 1. MNIST The MNIST dataset is popular and widely used in the fields of machine learning and computer vision. It consists of 70,000 grayscale images of handwritten digits 0–9, with 60,000 images for training and 10,000 for testing. Each image is 28x28 pixels in size and has a corresponding label denoting which digits it represents. This dataset can be downloaded from or loaded from : Kaggle Keras tf.keras.datasets.mnist.load_data(path="mnist.npz") 2. CIFAR-10 The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. It has a total of 50,000 training images and 10,000 test images which is further divided into five training batches and one test batch, each with 10,000 images. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.cifar10.load_data() 3. CIFAR-100 The CIFAR-100 dataset has 60,000 (50,000 training images and 10,000 test images) 32x32 colour images in 100 classes, with 600 images per class. The 100 classes are grouped into 20 super-classes, with a fine label to denote its class and a coarse label to represent the super-class that it belongs to. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.cifar100.load_data(label_mode="fine") 4. Fashion-MNIST The Fashion MNIST dataset was created by Zalando Research as a replacement for the original MNIST dataset. The Fashion MNIST dataset consists of 70,000 grayscale images(training set of 60,000 and a test set of 10,000) of clothing items. The images are 28x28 pixels in size and represent 10 different classes of clothing items, including ­T-shirts/tops, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots. It is similar to the original MNIST dataset, but with more challenging classification tasks due to the greater complexity and variety of the clothing items. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.fashion_mnist.load_data() 5. IMDB The IMDB dataset is commonly used for sentiment analysis tasks, where the goal is to classify the reviews as positive or negative based on their content. It consists of a collection of 50,000 movie reviews (training set of 25,000 and a test set of 25,000) from the Internet Movie Database website, split evenly between positive and negative reviews. Each review in this dataset is a text document, preprocessed and transformed into sequences of integers, where each integer represents a word in the review. The vocabulary size is limited to the 10,000 most frequent words in the dataset and any less frequent words are replaced with a special “unknown” token. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.imdb.load_data(
    path="imdb.npz",
    num_words=None,
    skip_top=0,
    maxlen=None,
    seed=113,
    start_char=1,
    oov_char=2,
    index_from=3,
    **kwargs
) 6. Boston Housing The Boston Housing dataset contains information about housing in the Boston area. This information consists of 506 instances (404 training and 102 test instances), with attributes for each instance. The attributes have a mix of quantitative and categorical variables, such as the average number of rooms per dwelling, per capita crime rate and the proportion of non-retail business acres per town. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.boston_housing.load_data(
    path="boston_housing.npz", test_split=0.2, seed=113
) 7. Wine Quality The Wine Quality dataset contains information on red and white wine samples. The goal of this dataset is to classify the quality of the wine based on chemical properties like pH, density, alcohol content and citric acid content. The variables in this dataset include: Fixed Acidity - The number of fixed acids in the wine, expressed in g/dm^3. Volatile Acidity - The number of volatile acids in the wine, expressed in g/dm^3. Citric Acid - The amount of citric acid in the wine, expressed in g/dm^3. Residual Sugar: The amount of residual sugar in the wine, expressed in g/dm^3. Chlorides - The amount of chloride in the wine, expressed in g/dm^3. Free Sulfur Dioxide - The amount of free sulfur dioxide in the wine, expressed in mg/dm^3. Total Sulfur Dioxide - The amount of total sulfur dioxide in the wine, expressed in mg/dm^3. Density - The density of the wine, expressed in g/cm^3. pH - The pH level of the wine. Sulphates - The number of sulphates in the wine, expressed in g/dm^3. Alcohol - The alcohol content of the wine, expressed in % vol. Quality - The quality rating of the wine, on a scale of 0 to 10. You can download the dataset , or it can be loaded from Keras: here from keras.datasets import wine_quality

(X_train, y_train), (X_test, y_test) = wine_quality.load_data(test_split=0.2, seed=113) 8. Reuters Newswire The Reuters Newswire dataset is a preprocessed version of the original Reuters dataset, with the text encoded as sequences of integers. It consists of 11,228 news articles with a vocabulary of 30,979 words. Each article is classified into one of 46 different topics like “corn”, “crude”, “earnings” and “acquisitions”. You can download the dataset from , or it can be loaded from : Kaggle Keras tf.keras.datasets.reuters.load_data(path="reuters.npz",num_words=None,skip_top=0,
maxlen=None,test_split=0.2,seed=113,start_char=1,oov_char=2,index_from=3,**kwargs) 9. Pima Indians Diabetes This dataset consists of medical data about Pima Indian women, such as age, number of pregnancies, glucose levels, blood pressure, skin thickness, BMI and insulin level. The Keras version of the Pima Indians Diabetes dataset contains 768 samples with 8 input variables and 1 output variable. The Pima Indians Diabetes dataset can be downloaded on , or it can be loaded from Keras: Kaggle from tensorflow.keras.datasets import pima_indians_diabetes

(x_train, y_train), (x_test, y_test) = pima_indians_diabetes.load_data() 10. Dogs vs Cats The Dogs vs Cats dataset consists of 25,000 labelled images of dogs and cats, with 12,500 images of each class. These images were collected from various sources with varying sizes and quality. You can download the dataset from , or it can be loaded from Keras: Kaggle # Import the necessary Keras libraries:
from keras.preprocessing.image import ImageDataGenerator

# Set the paths to the training and validation directories:
train_dir = 'path/to/train'
validation_dir = 'path/to/validation'

# Define an ImageDataGenerator object to perform data augmentation and normalization:
train_datagen = ImageDataGenerator(rescale=1./255,
                                   rotation_range=40,
                                   width_shift_range=0.2,
                                   height_shift_range=0.2,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

# Use flow_from_directory to load directory data in Keras:
validation_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(train_dir,
                                                    target_size=(150, 150),
                                                    batch_size=32,
                                                    class_mode='binary')

validation_generator = validation_datagen.flow_from_directory(validation_dir,
                                                              target_size=(150, 150),
                                                              batch_size=32,
                                                              class_mode='binary')

# The flow_from_directory yields preprocessed image batches and labels as DirectoryIterator. Note that in the above code, we are using data augmentation to create variations of the training images to help prevent overfitting. The validation data is not augmented. Common Use Cases for Keras Datasets - Handwritten digit recognition. MNIST - Object recognition in images. CIFAR-10 - Object recognition in images (more fine-grained than CIFAR-10). CIFAR-100 - Clothing item recognition. Fashion-MNIST - Sentiment analysis on movie reviews. IMDB - Regression on housing prices. Boston Housing - Classification of wine quality. Wine Quality - Topic classification of news articles. Reuters Newswire - Binary classification of diabetes in Pima Indian women. Pima Indians Diabetes - Binary classification of images of dogs and cats. Dogs vs Cats Final Thoughts Keras datasets are a valuable resource for machine learning practitioners and researchers, which can save time and effort in data collection and preprocessing, allowing for more focus on model development and experimentation. These Keras datasets are also available for anyone to download and use freely. More Dataset Listicles: Power Bi Datasets Time Series Datasets Geospatial Datasets Hugging Datasets R Datasets

Stories, News & Mentions about ZALANDO

Zalando’s Stories on HackerNoon

Zalando's News & Mentions